There is no shortage of sources of knowledge when it comes to use case applications for generative AI. In fact, ChatGPT itself has no end of advice for people looking for use cases.
However, this form of advice often fails to consider the nuance of a company’s situation or go beyond a surface-level understanding of the problem it is being asked to solve. The best use cases are frequently conjured from a mix of intimate knowledge of consumers (internal or external), an honest assessment of capabilities and a single-mindedness of what will generate value for the business and its customers.
Without wishing to put ChatGPT out of a job, there are several common areas ripe with use cases for generative AI:
1.Replace onerous processes with a conversational user interface (UI)
- e.g., help consumers fill out complex forms that witness high levels of dropout—mortgage applications or insurance claims
- e.g., streamline colleagues having to log into multiple systems to manage customer service interactions in a contact center
2.Enhance human understanding and productivity
- e.g., the world is full of reports that nobody reads—summarize the findings and share a concise digest with colleagues
- e.g., analyzing the huge amount of unstructured data businesses collect but never making sense of it
3.Automate the boring stuff
- e.g., add intelligence to robotic process automation (RPA) through natural language understanding
- e.g., automate the process of checking marketing assets against brand guidelines to focus resources on items that fail the test and remove the effort on items that clearly pass
There is no shortage of use cases or even methods for generating use cases. The art is ensuring that prioritization is given to those that are viable, feasible and desirable.